Acta Geographica Sinica ›› 2011, Vol. 66 ›› Issue (9): 1270-1280.doi: 10.11821/xb201109012

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An Efficient Quantitative Sensitivity Analysis Approach for Hydrological Model Parameters Using RSMSobol Method

KONG Fanzhe1, SONG Xiaomeng1, ZHAN Chesheng2, YE Aizhong3   

  1. 1. School of Resource and Earth Science, China University of Mining & Technology, Xuzhou 221008, Jiangsu, China;
    2. Key Laboratory of Water Cycle & Related Land Surface Processes,Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    3. State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science,Beijing Normal University, Beijing 100875, China
  • Received:2011-05-02 Revised:2011-06-08 Online:2011-09-20 Published:2011-09-20
  • Supported by:

    National Grand Science and Technology Special Project of Water Pollution Control and Improvement, No.2009ZX07210-006; National Key Basic Research Program of China (973 Program), No.2010CB428403; China University of Mining and Technology Student Innovation Project of Fundamental Research Funds for Central Universities

Abstract: Sensitivity analysis of hydrological models is a key step for model uncertainty quantification. It can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. However, how to effectively validate a model and identify the dominant parameters for a large-scale complex distributed hydrological model is a bottle-neck to achieve the parameters optimization. There are some shortcomings for classical approaches, e.g. time-consuming and high computation cost, to quantitatively assess the sensitivity of the multi-parameters complex hydrological model. For this reason, a new approach was applied in this paper, in which the support vector machine was used to construct the response surface (a surrogate model) at first. Then it integrated the SVM-based response surface with the Sobol method, i.e. the RSMSobol method, to achieve the quantification assessment of sensitivity for complex models. Taking the distributed time-variant gain model in the Huaihe River Basin as a case study, we selected three objective functions (i.e. water balance coefficient WB, Nash-Sutcliffe efficiency coefficient NS, and correlation coefficient RC) to assess the model as the output responses for sensitivity analysis. The results show that the RSMSobol method can not only achieve the quantification of the sensitivity, and also reduce the computational cost, with good accuracy compared to the classical approaches.

Key words: meta-modeling approach, response surface methodology, sensitivity analysis, support vector machines, Huaihe River Basin